Overview

Dataset statistics

Number of variables12
Number of observations373142
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical3

Alerts

fps_lags is highly correlated with fps_meanHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_std and 1 other fieldsHigh correlation
dropped_frames_std is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
dropped_frames_max is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
auto_fec_state is highly correlated with auto_fec_meanHigh correlation
auto_fec_mean is highly correlated with auto_fec_stateHigh correlation
rtt_mean is highly correlated with rtt_stdHigh correlation
rtt_std is highly correlated with rtt_meanHigh correlation
fps_mean is highly correlated with fps_std and 1 other fieldsHigh correlation
fps_std is highly correlated with fps_meanHigh correlation
rtt_mean is highly skewed (γ1 = 23.84983086) Skewed
rtt_std is highly skewed (γ1 = 54.33820743) Skewed
dropped_frames_mean is highly skewed (γ1 = 66.68466981) Skewed
dropped_frames_std is highly skewed (γ1 = 70.56907366) Skewed
dropped_frames_max is highly skewed (γ1 = 55.38695193) Skewed
fps_std has 64561 (17.3%) zeros Zeros
fps_lags has 349405 (93.6%) zeros Zeros
rtt_std has 6770 (1.8%) zeros Zeros
dropped_frames_mean has 343748 (92.1%) zeros Zeros
dropped_frames_std has 344002 (92.2%) zeros Zeros
dropped_frames_max has 343748 (92.1%) zeros Zeros
auto_fec_mean has 42564 (11.4%) zeros Zeros

Reproduction

Analysis started2022-10-02 19:54:38.515986
Analysis finished2022-10-02 19:55:04.568068
Duration26.05 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

fps_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct682
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.06981203
Minimum0
Maximum127.1
Zeros579
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:04.664493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.5
Q128.2
median30
Q344.1
95-th percentile57.6
Maximum127.1
Range127.1
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation11.39201155
Coefficient of variation (CV)0.3248381127
Kurtosis-0.3254180592
Mean35.06981203
Median Absolute Deviation (MAD)3.9
Skewness0.7298250765
Sum13086019.8
Variance129.7779272
MonotonicityNot monotonic
2022-10-02T22:55:04.755326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3059079
 
15.8%
29.914899
 
4.0%
29.88764
 
2.3%
29.76567
 
1.8%
30.16020
 
1.6%
29.65424
 
1.5%
29.54676
 
1.3%
29.43927
 
1.1%
253913
 
1.0%
29.33397
 
0.9%
Other values (672)256476
68.7%
ValueCountFrequency (%)
0579
0.2%
0.18
 
< 0.1%
0.26
 
< 0.1%
0.33
 
< 0.1%
0.49
 
< 0.1%
0.55
 
< 0.1%
0.65
 
< 0.1%
0.75
 
< 0.1%
0.87
 
< 0.1%
0.93
 
< 0.1%
ValueCountFrequency (%)
127.11
< 0.1%
125.81
< 0.1%
1111
< 0.1%
102.71
< 0.1%
98.71
< 0.1%
95.41
< 0.1%
91.81
< 0.1%
83.71
< 0.1%
831
< 0.1%
80.21
< 0.1%

fps_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23910
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.489202809
Minimum0
Maximum312.5408418
Zeros64561
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:04.852881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4216370214
median1.229272594
Q32.836272985
95-th percentile9.890511727
Maximum312.5408418
Range312.5408418
Interquartile range (IQR)2.414635963

Descriptive statistics

Standard deviation3.80523841
Coefficient of variation (CV)1.52869762
Kurtosis377.5371754
Mean2.489202809
Median Absolute Deviation (MAD)0.9206628052
Skewness7.748795453
Sum928826.1145
Variance14.47983936
MonotonicityNot monotonic
2022-10-02T22:55:04.939369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064561
 
17.3%
0.3162277666147
 
1.6%
0.3162277664766
 
1.3%
0.3162277663998
 
1.1%
0.3162277663950
 
1.1%
0.3162277663034
 
0.8%
0.42163702142034
 
0.5%
0.47140452081962
 
0.5%
0.42163702141945
 
0.5%
0.94868329811930
 
0.5%
Other values (23900)278815
74.7%
ValueCountFrequency (%)
064561
17.3%
0.316227766202
 
0.1%
0.3162277664766
 
1.3%
0.3162277663034
 
0.8%
0.3162277663950
 
1.1%
0.3162277662
 
< 0.1%
0.3162277666147
 
1.6%
0.3162277663998
 
1.1%
0.316227766408
 
0.1%
0.316227766156
 
< 0.1%
ValueCountFrequency (%)
312.54084181
< 0.1%
307.16727261
< 0.1%
307.00613461
< 0.1%
151.71230081
< 0.1%
148.8989591
< 0.1%
141.15637511
< 0.1%
139.65676181
< 0.1%
103.27358491
< 0.1%
98.057182861
< 0.1%
96.997651751
< 0.1%

fps_lags
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09737043806
Minimum0
Maximum10
Zeros349405
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:05.140807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5675633914
Coefficient of variation (CV)5.828908677
Kurtosis191.4111136
Mean0.09737043806
Median Absolute Deviation (MAD)0
Skewness12.28201418
Sum36333
Variance0.3221282032
MonotonicityNot monotonic
2022-10-02T22:55:05.201393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0349405
93.6%
119459
 
5.2%
22384
 
0.6%
10672
 
0.2%
3570
 
0.2%
4203
 
0.1%
5175
 
< 0.1%
697
 
< 0.1%
864
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
0349405
93.6%
119459
 
5.2%
22384
 
0.6%
3570
 
0.2%
4203
 
0.1%
5175
 
< 0.1%
697
 
< 0.1%
761
 
< 0.1%
864
 
< 0.1%
952
 
< 0.1%
ValueCountFrequency (%)
10672
 
0.2%
952
 
< 0.1%
864
 
< 0.1%
761
 
< 0.1%
697
 
< 0.1%
5175
 
< 0.1%
4203
 
0.1%
3570
 
0.2%
22384
 
0.6%
119459
5.2%

rtt_mean
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct6078
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.28192082
Minimum0
Maximum12898.4
Zeros919
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:05.276323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.4
Q116
median34
Q359.2
95-th percentile192.9
Maximum12898.4
Range12898.4
Interquartile range (IQR)43.2

Descriptive statistics

Standard deviation135.6887139
Coefficient of variation (CV)2.368787777
Kurtosis1048.927412
Mean57.28192082
Median Absolute Deviation (MAD)19.9
Skewness23.84983086
Sum21374290.5
Variance18411.42707
MonotonicityNot monotonic
2022-10-02T22:55:05.365249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.11043
 
0.3%
13.41016
 
0.3%
13.3997
 
0.3%
5.4991
 
0.3%
13.2989
 
0.3%
13971
 
0.3%
5.3962
 
0.3%
5.2959
 
0.3%
12.9954
 
0.3%
5.5949
 
0.3%
Other values (6068)363311
97.4%
ValueCountFrequency (%)
0919
0.2%
0.21
 
< 0.1%
0.35
 
< 0.1%
0.42
 
< 0.1%
0.56
 
< 0.1%
0.62
 
< 0.1%
0.79
 
< 0.1%
0.86
 
< 0.1%
0.93
 
< 0.1%
110
 
< 0.1%
ValueCountFrequency (%)
12898.41
< 0.1%
105911
< 0.1%
97951
< 0.1%
9021.41
< 0.1%
8661.11
< 0.1%
8360.11
< 0.1%
78361
< 0.1%
7469.21
< 0.1%
7191.81
< 0.1%
71691
< 0.1%

rtt_std
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct65454
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.22966567
Minimum0
Maximum40721.93329
Zeros6770
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:05.456976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4216370214
Q10.7888106377
median1.712697677
Q36.196773354
95-th percentile47.54591348
Maximum40721.93329
Range40721.93329
Interquartile range (IQR)5.407962716

Descriptive statistics

Standard deviation163.1101173
Coefficient of variation (CV)7.683122281
Kurtosis10634.61935
Mean21.22966567
Median Absolute Deviation (MAD)1.196299898
Skewness54.33820743
Sum7921679.907
Variance26604.91037
MonotonicityNot monotonic
2022-10-02T22:55:05.544447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06770
 
1.8%
0.48304589152611
 
0.7%
0.51639777952407
 
0.6%
0.42163702142191
 
0.6%
0.3162277662134
 
0.6%
0.51639777951960
 
0.5%
0.56764621221834
 
0.5%
0.67494855771783
 
0.5%
0.3162277661732
 
0.5%
0.69920589881641
 
0.4%
Other values (65444)348079
93.3%
ValueCountFrequency (%)
06770
1.8%
0.3162277662
 
< 0.1%
0.3162277662
 
< 0.1%
0.3162277663
 
< 0.1%
0.3162277668
 
< 0.1%
0.3162277662
 
< 0.1%
0.31622776627
 
< 0.1%
0.31622776635
 
< 0.1%
0.31622776629
 
< 0.1%
0.316227766241
 
0.1%
ValueCountFrequency (%)
40721.933291
< 0.1%
6025.5310781
< 0.1%
5769.0170831
< 0.1%
5596.6950961
< 0.1%
5413.645821
< 0.1%
5215.5416931
< 0.1%
5123.8386321
< 0.1%
4986.0478331
< 0.1%
4969.8872511
< 0.1%
4931.5987941
< 0.1%

dropped_frames_mean
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct915
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346235.0058
Minimum0
Maximum2097288600
Zeros343748
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:05.639516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.5
Maximum2097288600
Range2097288600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20301923.45
Coefficient of variation (CV)58.63625313
Kurtosis4776.169824
Mean346235.0058
Median Absolute Deviation (MAD)0
Skewness66.68466981
Sum1.291948226 × 1011
Variance4.121680956 × 1014
MonotonicityNot monotonic
2022-10-02T22:55:05.724666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0343748
92.1%
3.3928
 
0.2%
3.4906
 
0.2%
3.5717
 
0.2%
3.2595
 
0.2%
3.6580
 
0.2%
6442
 
0.1%
6.6428
 
0.1%
0.1420
 
0.1%
3.7417
 
0.1%
Other values (905)23961
 
6.4%
ValueCountFrequency (%)
0343748
92.1%
0.1420
 
0.1%
0.2133
 
< 0.1%
0.3108
 
< 0.1%
0.4120
 
< 0.1%
0.5105
 
< 0.1%
0.6121
 
< 0.1%
0.7120
 
< 0.1%
0.8110
 
< 0.1%
0.9115
 
< 0.1%
ValueCountFrequency (%)
20972886001
< 0.1%
19367460001
< 0.1%
19360914001
< 0.1%
17992902001
< 0.1%
17350231002
< 0.1%
16849554001
< 0.1%
16822012001
< 0.1%
16679112001
< 0.1%
16654208001
< 0.1%
16397078801
< 0.1%

dropped_frames_std
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct6578
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149161.6745
Minimum0
Maximum996375136.4
Zeros344002
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:05.813975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.28156617
Maximum996375136.4
Range996375136.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9614594.737
Coefficient of variation (CV)64.45754091
Kurtosis5302.137749
Mean149161.6745
Median Absolute Deviation (MAD)0
Skewness70.56907366
Sum5.565848556 × 1010
Variance9.244043196 × 1013
MonotonicityNot monotonic
2022-10-02T22:55:05.903206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0344002
92.2%
10.43551628917
 
0.2%
11.06797181700
 
0.2%
10.75174404624
 
0.2%
10.11928851585
 
0.2%
11.38419958568
 
0.2%
0.316227766422
 
0.1%
11.70042734409
 
0.1%
18.97366596374
 
0.1%
18.65743819351
 
0.1%
Other values (6568)24190
 
6.5%
ValueCountFrequency (%)
0344002
92.2%
0.316227766422
 
0.1%
0.42163702141
 
< 0.1%
0.42163702145
 
< 0.1%
0.483045891517
 
< 0.1%
0.48304589151
 
< 0.1%
0.51639777951
 
< 0.1%
0.51639777955
 
< 0.1%
0.51639777952
 
< 0.1%
0.52704627673
 
< 0.1%
ValueCountFrequency (%)
996375136.41
< 0.1%
988728405.31
< 0.1%
935537198.31
< 0.1%
902305725.81
< 0.1%
864826861.51
< 0.1%
851095768.91
< 0.1%
838095780.21
< 0.1%
825622032.51
< 0.1%
824661111.71
< 0.1%
815553396.81
< 0.1%

dropped_frames_max
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct374
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean522273.6655
Minimum0
Maximum2097288600
Zeros343748
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:05.994623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile39
Maximum2097288600
Range2097288600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27490801.77
Coefficient of variation (CV)52.63677567
Kurtosis3184.846192
Mean522273.6655
Median Absolute Deviation (MAD)0
Skewness55.38695193
Sum1.948822401 × 1011
Variance7.55744182 × 1014
MonotonicityNot monotonic
2022-10-02T22:55:06.079717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0343748
92.1%
331185
 
0.3%
341119
 
0.3%
35858
 
0.2%
60807
 
0.2%
36752
 
0.2%
32683
 
0.2%
59679
 
0.2%
48542
 
0.1%
37525
 
0.1%
Other values (364)22244
 
6.0%
ValueCountFrequency (%)
0343748
92.1%
1468
 
0.1%
2134
 
< 0.1%
3111
 
< 0.1%
4126
 
< 0.1%
5106
 
< 0.1%
6128
 
< 0.1%
7116
 
< 0.1%
8112
 
< 0.1%
9120
 
< 0.1%
ValueCountFrequency (%)
20972886002
 
< 0.1%
19367460004
 
< 0.1%
19360914002
 
< 0.1%
18527962003
 
< 0.1%
17992902002
 
< 0.1%
17638382001
 
< 0.1%
173502310010
< 0.1%
16849554003
 
< 0.1%
16822012005
< 0.1%
16679112006
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
1
299958 
2
72774 
3
 
410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters373142
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1299958
80.4%
272774
 
19.5%
3410
 
0.1%

Length

2022-10-02T22:55:06.158324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T22:55:06.340719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1299958
80.4%
272774
 
19.5%
3410
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1299958
80.4%
272774
 
19.5%
3410
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number373142
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1299958
80.4%
272774
 
19.5%
3410
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common373142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1299958
80.4%
272774
 
19.5%
3410
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII373142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1299958
80.4%
272774
 
19.5%
3410
 
0.1%

auto_fec_state
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
1
330578 
2
42564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters373142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1330578
88.6%
242564
 
11.4%

Length

2022-10-02T22:55:06.400470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T22:55:06.467704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1330578
88.6%
242564
 
11.4%

Most occurring characters

ValueCountFrequency (%)
1330578
88.6%
242564
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number373142
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1330578
88.6%
242564
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common373142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1330578
88.6%
242564
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII373142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1330578
88.6%
242564
 
11.4%

auto_fec_mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct118
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.95277937
Minimum0
Maximum250
Zeros42564
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2022-10-02T22:55:06.534232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median50
Q350
95-th percentile100
Maximum250
Range250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35.50679906
Coefficient of variation (CV)0.6834436865
Kurtosis8.717465395
Mean51.95277937
Median Absolute Deviation (MAD)0
Skewness2.406901665
Sum19385764
Variance1260.732779
MonotonicityNot monotonic
2022-10-02T22:55:06.620409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50285064
76.4%
042564
 
11.4%
10020554
 
5.5%
20013457
 
3.6%
206747
 
1.8%
402663
 
0.7%
60235
 
0.1%
22159
 
< 0.1%
42133
 
< 0.1%
4482
 
< 0.1%
Other values (108)1484
 
0.4%
ValueCountFrequency (%)
042564
11.4%
42
 
< 0.1%
519
 
< 0.1%
61
 
< 0.1%
85
 
< 0.1%
1026
 
< 0.1%
122
 
< 0.1%
141
 
< 0.1%
1517
 
< 0.1%
206747
 
1.8%
ValueCountFrequency (%)
2507
 
< 0.1%
2351
 
< 0.1%
2301
 
< 0.1%
2201
 
< 0.1%
2101
 
< 0.1%
20013457
3.6%
1952
 
< 0.1%
19014
 
< 0.1%
18524
 
< 0.1%
1822
 
< 0.1%

stream_quality
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
0
349288 
1
 
23854

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters373142
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0349288
93.6%
123854
 
6.4%

Length

2022-10-02T22:55:06.698223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T22:55:06.765243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0349288
93.6%
123854
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0349288
93.6%
123854
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number373142
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0349288
93.6%
123854
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common373142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0349288
93.6%
123854
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII373142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0349288
93.6%
123854
 
6.4%

Interactions

2022-10-02T22:55:02.604427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:52.929856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:54.452339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.667189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.790594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.965013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.101579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.215936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.484606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.732731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:53.207144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:54.601380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.785616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.910286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.093549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.228784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.342663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.609465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.851459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:53.345449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:54.746303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.903709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.027156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.214734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.347939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.468611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.730217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.968582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:53.502836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:54.884546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.024578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.263066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.334298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.467432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.592552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.851522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:03.103265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:53.661660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.020095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.179139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.379951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.458638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.586828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.722570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.984591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:03.244494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:53.827564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.157263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.316428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.501935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.591737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.710323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.855736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.119644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:03.364130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:53.987448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.284747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.447493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.616632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.722849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.832888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.987936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.241253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:03.483498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:54.149746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.411986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.562353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.732624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.854547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:59.968937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.233889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.357593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:03.615459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:54.301767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:55.543331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:56.675822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:57.848865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:54:58.975056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:00.097028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:01.354932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:55:02.478585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-02T22:55:06.821856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T22:55:07.186597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T22:55:07.305727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T22:55:07.415316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-02T22:55:07.499749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T22:55:03.740195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T22:55:04.036521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality
024.40.516398091.16.7239210.00.00.01150.00
128.62.065591099.715.9237770.00.00.01150.00
230.00.000000098.111.7987760.00.00.01150.00
330.30.948683099.413.0145220.00.00.01150.00
429.90.3162280123.262.4763070.00.00.01150.00
529.51.6499160131.2114.2577980.00.00.01150.00
624.30.483046098.316.4994950.00.00.01150.00
724.50.9718250141.9103.8144180.00.00.01150.00
830.00.0000000107.518.7335110.00.00.01150.00
930.00.4714050108.210.9524220.00.00.01150.00

Last rows

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality
37313255.03.1269440119.60.5163980.00.0000000.02150.00
37313354.65.6803760119.50.7071070.00.0000000.02150.00
37313454.71.0593500119.90.8755950.00.0000000.02150.00
37313553.91.3703200119.70.4830460.00.0000000.02150.00
37313653.23.4253950123.412.1673520.00.0000000.02150.00
37313748.312.3022130119.30.6749498.526.87936085.02150.00
37313848.78.9573060119.80.7888116.821.50348868.02150.00
37313954.27.0992960119.40.5163980.00.0000000.02150.00
37314049.57.5755450120.00.8164970.00.0000000.02150.00
37314144.97.7237010107.437.7394580.00.0000000.02150.00